|
|
|
|
|
| Developing a process-based and remote sensing driven crop yield model for maize (PRYM–Maize) and its validation over the Northeast China Plain |
| ZHANG Sha1, 2, 3, Bai Yun2, 3, Zhang Jia-hua2, 3, 4, Shahzad ALI1, 2 |
1 School of Automation, Qingdao University, Qingdao 266071, P.R.China
2 College of Computer Science and Technology, Qingdao University, Qingdao 266071, P.R.China
3 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, P.R.China
4 University of Chinese Academy of Sciences, Beijing 100049, P.R.China |
|
|
|
|
Abstract Spatial dynamics of crop yield provide useful information for improving the production. High sensitivity of crop growth models to uncertainties in input factors and parameters and relatively coarse parameterizations in conventional remote sensing (RS) approaches limited their applications over broad regions. In this study, a process-based and remote sensing driven crop yield model for maize (PRYM–Maize) was developed to estimate regional maize yield, and it was implemented using eight data-model coupling strategies (DMCSs) over the Northeast China Plain (NECP). Simulations under eight DMCSs were validated against the prefecture-level statistics (2010–2012) reported by National Bureau of Statistics of China, and inter-compared. The 3-year averaged result could give more robust estimate than the yearly simulation for maize yield over space. A 3-year averaged validation showed that prefecture-level estimates by PRYM–Maize under DMCS8, which coupled with the development stage (DVS)-based grain-filling algorithm and RS phenology information and leaf area index (LAI), had higher correlation (R, 0.61) and smaller root mean standard error (RMSE, 1.33 t ha–1) with the statistics than did PRYM–Maize under other DMCSs. The result also demonstrated that DVS-based grain-filling algorithm worked better for maize yield than did the harvest index (HI)-based method, and both RS phenology information and LAI worked for improving regional maize yield estimate. These results demonstrate that the developed PRYM–Maize under DMCS8 gives reasonable estimates for maize yield and provides scientific basis facilitating the understanding the spatial variations of maize yield over the NECP.
|
|
Received: 02 April 2020
Accepted:
|
| Fund: This study was supported by the National Key Research and Development Program of China (2016YFD0300101, and 2016YFD0300110), the National Natural Science Foundation of China (41871253 and 31671585), the “Taishan Scholar” Project of Shandong Province, China, and the Key Basic Research Project of Shandong Natural Science Foundation, China (ZR2017ZB0422). |
|
Corresponding Authors:
ZHANG Jia-hua, E-mail: zhangjh@radi.ac.cn; BAI Yun, E-mail: baiyun@qdu.edu.cn
|
Cite this article:
ZHANG Sha, Bai Yun, Zhang Jia-hua, Shahzad ALI.
2021.
Developing a process-based and remote sensing driven crop yield model for maize (PRYM–Maize) and its validation over the Northeast China Plain. Journal of Integrative Agriculture, 20(2): 408-423.
|
| Amarasingha R P R K, Suriyagoda L D B, Marambe B, Gaydon D S, Galagedara L W, Punyawardena R, Silva G L L P, Nidumolu U, Howden M. 2015. Simulation of crop and water productivity for rice (Oryza sativa L.) using APSIM under diverse agro-climatic conditions and water management techniques in Sri Lanka. Agricultural Water Management, 160, 132–143. Amthor J S. 1984. The role of maintenance respiration in plant growth. Plant, Cell & Environment, 7, 561–569. Bai Y. 2019. Exploring the yield and resource use efficiency gaps of maize over the major cultivation areas of maize in China: Modeling, simulations and analyses with remote sensing. Ph D thesis, Institute of Remote Sensing and Digital Earth, Chinese Academy of Science. University of Chinese Academy of Sciences, Beijing. (in Chinese) Bai Y, Zhang J, Zhang S, Koju U A, Yao F, Igbawua T. 2017. Using precipitation, vertical root distribution and satellite-retrieved vegetation information to parameterize water stress in a Penman-Monteith approach to evapotranspiration modelling under Mediterranean climate. Journal of Advances in Modeling Earth Systems, 9, 168–192. Bai Y, Zhang J, Zhang S, Yao F, Magliulo V. 2018. A remote sensing-based two-leaf canopy conductance model: Global optimization and applications in modeling gross primary productivity and evapotranspiration of crops. Remote Sensing of Environment, 215, 411–437. Balkovi? J, van der Velde M, Schmid E, Skalský R, Khabarov N, Obersteiner M, Stürmer B, Xiong W. 2013. Pan-European crop modelling with EPIC: Implementation, up-scaling and regional crop yield validation. Agricultural Systems, 120, 61–75. Basso B, Liu L, Ritchie J T. 2016. A comprehensive review of the CERES-wheat, -maize and -rice models’ performances. Advances in Agronomy, 136, 27–132. Berry J A, Farquhar G D. 1978. The CO2 concentrating function of C4 photosynthesis a biochemical model. In: The Proceedings of the Fourth International Congress on Photosynthesis. Springer, Dordrecht, Netherlands. Bonan G B. 1995. Land-atmosphere CO2 exchange simulated by a land surface process model coupled to an atmospheric general circulation model. Journal of Geophysical Research Atmospheres, 100, 2817–2831. Caemmerer S V. 2000. Biochemical models of leaf phoosynthesis. CSIRO PUBLISHING, Collingwood VIC, Australia. Chen C, Wang E, Yu Q. 2010. Modelling the effects of climate variability and water management on crop water productivity and water balance in the North China Plain. Agricultural Water Management, 97, 1175–1184. Chen J M, Liu J, Cihlar J, Goulden M L. 1999. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications. Ecological Modelling, 124, 99–119. Chen R, Ersi K, Yang J, Lu S, Zhao W. 2004. Validation of five global radiation models with measured daily data in China. Energy Conversion and Management, 45, 1759–1769. Collatz G J, Ribascarbo M, Berry J A. 1992. Coupled photosynthesis-stomatal conductance model for leaves of C4 plants. Functional Plant Biology, 19, 519–538. Curnel Y, de Wit A J W, Duveiller G, Defourny P. 2011. Potential performances of remotely sensed LAI assimilation in WOFOST model based on an OSS experiment. Agricultural and Forest Meteorology, 151, 1843–1855. Duffie J A, Beckman W A. 2013. Solar Radiation. Solar Engineering of Thermal Processes. John Wiley & Sons, US. pp. 3–42. Gilardelli C, Stella T, Confalonieri R, Ranghetti L, Campos-Taberner M, García-Haro F J, Boschetti M. 2019. Downscaling rice yield simulation at sub-field scale using remotely sensed LAI data. European Journal of Agronomy, 103, 108–116. Grassini P, van Bussel L G J, Van Wart J, Wolf J, Claessens L, Yang H, Boogaard H, de Groot H, van Ittersum M K, Cassman K G. 2015. How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis. Field Crops Research, 177, 49–63. Hou H P, Ma W, Noor M A, Tang L Y, Li C F, Ding Z S, Zhao M. 2020. Quantitative design of yield components to simulate yield formation for maize in China. Journal of Integrative Agriculture, 19, 668–679. Huang J, Tian L, Liang S, Ma H, Becker-Reshef I, Huang Y, Su W, Zhang X, Zhu D, Wu W. 2015. Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model. Agricultural and Forest Meteorology, 204, 106–121. Huang Y, Ryu Y, Jiang C, Kimm H, Kim S, Kang M, Shim K. 2018. BESS-Rice: A remote sensing derived and biophysical process-based rice productivity simulation model. Agricultural and Forest Meteorology, 256–257, 253–269. Innes P J, Tan D K Y, Van Ogtrop F, Amthor J S. 2015. Effects of high-temperature episodes on wheat yields in New South Wales, Australia. Agricultural and Forest Meteorology, 208, 95–107. van Ittersum M K, Cassman K G, Grassini P, Wolf J, Tittonell P, Hochman Z. 2013. Yield gap analysis with local to global relevance - A review. Field Crops Research, 143, 4–17. Jiang C, Ryu Y. 2016. Multi-scale evaluation of global gross primary productivity and evapotranspiration products derived from Breathing Earth System Simulator (BESS). Remote Sensing of Environment, 186, 528–547. Jin Z, Azzari G, Lobell D B. 2017. Improving the accuracy of satellite-based high-resolution yield estimation: A test of multiple scalable approaches. Agricultural and Forest Meteorology, 247, 207–220. Jin Z, Zhuang Q, Tan Z, Dukes J S, Zheng B, Melillo J M. 2016. Do maize models capture the impacts of heat and drought stresses on yield? Using algorithm ensembles to identify successful approaches. Global Change Biology, 22, 3112–3126. Ju W, Gao P, Zhou Y, Chen J M, Chen S, Li X. 2010. Prediction of summer grain crop yield with a process-based ecosystem model and remote sensing data for the northern area of the Jiangsu Province, China. International Journal of Remote Sensing, 31, 1573–1587. Kim S, Gitz D, Sicher R, Baker J, Timlin D, Reddy V. 2007. Temperature dependence of growth, development, and photosynthesis in maize under elevated CO2. Environmental and Experimental Botany, 61, 224–236. Leuning R. 1990. Modelling stomatal behaviour and photosynthesis of Eucalyptus grandis. Functional Plant Biology, 17, 159–175. Levis S, Bonan G B, Kluzek E, Thornton P E, Jones A, Sacks W J, Kucharik C J. 2012. Interactive crop management in the community earth system model (CESM1): Seasonal influences on land-atmosphere fluxes. Journal of Climate, 25, 4839–4859. Li H, Tang F Y, Wang J L, Tang K Y, Xu Y, Wang Z W. 2016. Simulation on dry matter distribution coefficient for summer maize in North China. Chinese Journal of Agrometeorology, 37, 335–342. (in Chinese) Li T, Feng Y, Li X. 2009. Predicting crop growth under different cropping and fertilizing management practices. Agricultural and Forest Meteorology, 149, 985–998. Liu J, Chen J M, Cihlar J, Chen W. 1999. Net primary productivity distribution in the BOREAS region from a process model using satellite and surface data. Journal of Geophysical Research: Atmospheres, 104, 27735–27754. Liu J, Yang K, Shi S, Zhao X. 2012. Maize Cultivation in North China. China Agricultural Science and Technology Press, Beijing. (in Chinese) Liu Z. 2013. The yield gaps and constraint factors of spring maize in Northeast China. Ph D thesis, China Agricultural University, Beijing. (in Chinese) Lobell D B. 2013. The use of satellite data for crop yield gap analysis. Field Crops Research, 143, 56–64. Lobell D B, Cassman K G, Field C B. 2009. Crop yield gaps: Their importance, magnitudes, and causes. Annual Review of Environment & Resources, 34, 179–204. Lobell D B, Ortiz-Monasterio J I, Sibley A M, Sohu V S. 2013. Satellite detection of earlier wheat sowing in India and implications for yield trends. Agricultural Systems, 115, 137–143. Lobell D B, Thau D, Seifert C, Engle E, Little B. 2015. A scalable satellite-based crop yield mapper. Remote Sensing of Environment, 164, 324–333. Lv S. 2016. Analysis of impacts of climate change on spring maize and adaptation in maize cultivars in Lishu Jilin. Ph D thesis, China Agricultural University, Beijing. (in Chinese) Mccown R L, Hammer G L, Hargreaves J N G, Holzworth D P, Freebairn D M. 1996. APSIM: A novel software system for model development, model testing and simulation in agricultural systems research. Agricultural Systems, 50, 255–271. Medlyn B E, Dreyer E, Ellsworth D, Forstreuter M, Harley P C, Kirschbaum M U F, Le Roux X, Montpied P, Strassemeyer J, Walcroft A, Wang K, Loustau D. 2002. Temperature response of parameters of a biochemically based model of photosynthesis. II. A review of experimental data. Plant, Cell & Environment, 25, 1167–1179. Mo X, Liu S, Lin Z. 2012. Evaluation of an ecosystem model for a wheat–maize double cropping system over the North China Plain. Environmental Modelling & Software, 32, 61–73. Norman J M. 1982. Simulation of microclimates A2. In: Hatfield J L, Thomason I J, eds., Biometeorology in Integrated Pest Management. Academic Press, California, US. pp. 65–99. Osborne T, Gornall J, Hooker J, Williams K, Wiltshire A, Betts R, Wheeler T. 2015. JULES-crop: A parametrisation of crops in the Joint UK Land Environment Simulator. Geoscientific Model Development, 8, 1139–1155. Ouyang Z. 2010. Chinese Ecosystem Research Dataset. Agriculture Ecosystems. Yucheng Station, Shandong: 1988–2006. China Agriculture Press, Beijing. (in Chinese) Peng B, Guan K, Chen M, Lawrence D M, Pokhrel Y, Suyker A, Arkebauer T, Lu Y. 2018. Improving maize growth processes in the community land model: Implementation and evaluation. Agricultural and Forest Meteorology, 250–251, 64–89. Ryu Y, Baldocchi D D, Kobayashi H, van Ingen C, Li J, Black T A, Beringer J, van Gorsel E, Knohl A, Law B E, Roupsard O. 2011. Integration of MODIS land and atmosphere products with a coupled-process model to estimate gross primary productivity and evapotranspiration from 1 km to global scales. Global Biogeochemical Cycles, 25, GB4017. Sinclair T R. 1998. Historical changes in harvest index and crop nitrogen accumulation. Crop Science, 38, 638–643. Son N T, Chen C F, Chen C R, Chang L Y, Duc H N, Nguyen L D. 2013. Prediction of rice crop yield using MODIS EVI−LAI data in the Mekong Delta, Vietnam. International Journal of Remote Sensing, 34, 7275–7292. Sun H, Zhang X, Wang E, Chen S, Shao L. 2015. Quantifying the impact of irrigation on groundwater reserve and crop production - A case study in the North China Plain. European Journal of Agronomy, 70, 48–56. Supit I, Hooijer A A, Van Diepen C A. 1994. System Description of the Wofost 6.0 Crop Simulation Model Implemente in CGMS. Volume 1: Theory and Algorithms. Joint Research Centre, European Commission, Brussels, Luxembourg. Tao F, Yokozawa M, Zhang Z, Xu Y, Hayashi Y. 2005. Remote sensing of crop production in China by production efficiency models: models comparisons, estimates and uncertainties. Ecological Modelling, 183, 385–396. Wang J, Wang E, Yin H, Feng L, Zhang J. 2014. Declining yield potential and shrinking yield gaps of maize in the North China Plain. Agricultural and Forest Meteorology, 195–196, 89–101. Wang J W, Zhang J H, Bai Y, Zhang S, Yang S S, Yao F M. 2020. Integrating remote sensing-based crop process model with environmental zonation scheme to estimate rice yield gap in Northeast China. Field Crop Research, 246, 107682. Wang P, Sun R, Zhang J, Zhou Y, Xie D, Zhu Q. 2011. Yield estimation of winter wheat in the North China Plain using the remote-sensing–photosynthesis–yield estimation for crops (RS–P–YEC) model. International Journal of Remote Sensing, 32, 6335–6348. Wang Y, Xu X, Huang L, Yang G, Fan L, Wei P, Chen G. 2019. An improved CASA model for estimating winter wheat yield from remote sensing images. Remote Sensing, 11, 1088. Wart J V, Kersebaum K C, Peng S, Milner M, Cassman K G. 2013. Estimating crop yield potential at regional to national scales. Field Crops Research, 143, 34–43. de Wit A, Duveiller G, Defourny P. 2012. Estimating regional winter wheat yield with WOFOST through the assimilation of green area index retrieved from MODIS observations. Agricultural and Forest Meteorology, 164, 39–52. Yang H S, Dobermann A, Lindquist J L, Walters D T, Arkebauer T J, Cassman K G. 2004. Hybrid-maize - A maize simulation model that combines two crop modeling approaches. Field Crops Research, 87, 131–154. Yang P, Chen Z, Zhou Q, Zha Y, Wu W, Shibasaki R. 2006. Comparisons of MODIS LAI products and LAI estimates derived from Landsat TM. IEEE International Conference on Geoscience & Remote Sensing Symposium, Denver, CO. pp. 3087–3097. Yang P, Shibasaki R, Wu W, Zhou Q, Chen Z, Zha Y, Shi Y, Tang H. 2007. Evaluation of MODIS land cover and LAI products in cropland of North China Plain using in situ measurements and landsat TM images. IEEE Transactions on Geoscience & Remote Sensing, 45, 3087–3097. Yao F, Tang Y, Wang P, Zhang J. 2015. Estimation of maize yield by using a process-based model and remote sensing data in the Northeast China Plain. Physics and Chemistry of the Earth (Parts A/B/C), 87–88, 142–152. Zhang J, Feng L, Yao F. 2014. Improved maize cultivated area estimation over a large scale combining MODIS–EVI time series data and crop phenological information. ISPRS Journal of Photogrammetry and Remote Sensing, 94, 102–113. Zhang J, Wang J, Xin X. 2010. Chinese Ecosystem Research Dataset. Agriculture Ecosystems. Fengqiu Station, Henan: 1988–2008. China Agriculture Press, Beijing. (in Chinese) Zhang S, Zhang J H, Bai Y, Xun L, Wang J W, Zhang D, Yang S S, Yuan J G. 2019. Developing a method to estimate maize area in north and northeast of China combining crop phenology information and time-series MODIS EVI. IEEE Access, 7, 144861–144873. Zhang X, Wang S, Sun H, Chen S, Shao L, Liu X. 2013. Contribution of cultivar, fertilizer and weather to yield variation of winter wheat over three decades: A case study in the North China Plain. European Journal of Agronomy, 50, 52–59. Zhao Y, Chen X, Lobell D B. 2016. An approach to understanding persistent yield variation - A case study in North China Plain. European Journal of Agronomy, 77, 10–19. |
| No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
